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--- |
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tags: |
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- ultralyticsplus |
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- yolov8 |
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- ultralytics |
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- yolo |
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- vision |
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- image-segmentation |
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- pytorch |
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library_name: ultralytics |
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library_version: 8.0.6 |
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inference: false |
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model-index: |
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- name: fcakyon/test-model |
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results: |
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- task: |
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type: image-segmentation |
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metrics: |
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- type: precision |
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value: 0.63311 |
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name: mAP@0.5(box) |
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- type: precision |
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value: 0.60214 |
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name: mAP@0.5(mask) |
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--- |
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<div align="center"> |
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<img width="640" alt="fcakyon/test-model" src="https://huggingface.co/fcakyon/test-model/resolve/main/thumbnail.jpg"> |
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</div> |
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### Supported Labels |
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``` |
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['Cracks-and-spalling', 'object'] |
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``` |
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### How to use |
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- Install [ultralytics](https://github.com/ultralytics/ultralytics) and [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus): |
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```bash |
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pip install -U ultralytics ultralyticsplus |
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``` |
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- Load model and perform prediction: |
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```python |
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from ultralyticsplus import YOLO, render_model_output |
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# load model |
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model = YOLO('fcakyon/test-model') |
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# set model parameters |
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model.overrides['conf'] = 0.25 # NMS confidence threshold |
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model.overrides['iou'] = 0.45 # NMS IoU threshold |
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model.overrides['agnostic_nms'] = False # NMS class-agnostic |
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model.overrides['max_det'] = 1000 # maximum number of detections per image |
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# set image |
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image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' |
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# perform inference |
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for result in model.predict(image, return_outputs=True): |
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print(result["det"]) # [[x1, y1, x2, y2, conf, class]] |
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print(result["segment"]) # [segmentation mask] |
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render = render_model_output(model=model, image=image, model_output=result) |
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render.show() |
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``` |
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